DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

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Manuscript for IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 8 algorithm selection (v) and resource allocation (w) as in (2). As stated earlier, we design a scalable control mechanism to achieve the objective in the framework of MPC by building a mathematical programming model and decentralizing it. arg max v,w e i ∑∑ i∈ A d = 1 v d i − CCT(T ) (2) 3. Mathematical programming model The mathematical programming model is essentially a scheduling problem formulation. There are a variety of formulations and algorithms available for diverse scheduling problems in the context of multiprocessor, manufacturing, and project management. In general, a scheduling problem is allocating limited resources to a set of tasks to optimize a specific objective. One widely studied objective is completion time (also called makespan in the manufacturing literature) as the problem we have considered. Though it is not easy to find a problem exactly same as ours, it is possible to convert our problem into one of the scheduling problems. For example, in job shop, there are a set of jobs and a set of machines. Each job has a set of serial operations and each operation should be processed on a specific machine. A job shop scheduling problem is sequencing the operations in each machine by satisfying a set of job precedence constraints such that the completion time is minimized. When we assign a value mode to each task, our problem can be exactly transformed into a job shop scheduling problem. However, scheduling problems are in general intractable. Though the job shop scheduling problem is polynomially solvable when there are two machines and each job has two operations, it becomes NP-hard on the number of jobs even if the number of machines or operations is more than two [19][20]. Considering that the task flow structure of our networks is arbitrary, our scheduling
Manuscript for IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 9 problem is NP-hard on the number of components in general. The increase of the number of tasks and consideration of alternative algorithms make the problem even harder. Moreover, there can be large number of nodes in our networks. Though it may be possible to use some available heuristic algorithms from the job shop scheduling problem by taking into account alternative algorithms, our scheduling problem has a particular characteristic, i.e., the number of tasks for each component can be large. Though the increase of the number of tasks adds more complexity, it can also lead us to develop an efficient heuristic programming model. In this section, we characterize an optimal resource allocation by analyzing the impacts of the largeness and subsequently build a mathematical programming model solvable in polynomial time. 3.1 Optimal resource allocation Consider current time t=0 and assume that each component uses a value mode common to all the tasks (i.e. pure strategy). We will discuss the optimality of the pure strategy later in this subsection. We define Load Index LI i which represents component i’s total CPU time required to process its tasks. As a component needs to process its own root tasks as well as incoming tasks from its predecessors, its number of tasks L i is identified as in (3), where i denotes the immediate predecessors of component i. Then, LI i is represented as in (4). ∑ L i = rti + La (3) a∈i LI = L f v ) (4) i To provide theoretical foundation of optimal resource allocation policy, we convert a network into a network with tasks having infinitesimal processing times. Each root task is divided into r infinitesimal tasks and each f i (v i ) is replaced with f i (v i )/r. Then, the load index of each component i i ( i
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Ultra*Log PSU/IAI Final Report for
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Contents Contents .................
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Executive Summary Ultra*Log is a De
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2.3 Gnanasambandam, N., Lee, S., Ku
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6 Characterization and analysis of
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timizing simultaneously the link de
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where ∆ 1 (j) ≥ 0 and ∆ 2 (i,
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Table 2. GA Results Agent N A D max
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connected component, in which a pat
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Random Small-world Scale-free with
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Growth mechanisms Start with a smal
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Table 2. The proposed network’s c
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DMAS Controller. The functional uni
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1000 500 0 -500 -1000 0.2 0.4 0.6 0
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tems. Proceedings of the Second Joi
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Proceedings of the 1st Open Cougaar
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1 SITUATION IDENTIFICATION USING DY
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3 3.2 Behavior In SSC society an ag
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5 All the behavior parameters may n
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Estimating Global Stress Environmen
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chaotic deterministic time series.
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100% 1 95% 2 14 15 64% TAO 4 64% 62
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interactions as a dynamical system
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ehavior states under varying system
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Extensive testing and validation of
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5 Conclusions and Future Research T
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2 to function critically even under
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4 points into a corresponding inner
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6 stresses well. The Warehouse 1 ag
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Table 1: Notation Symbol Descriptio
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4 Conclusions and Future Work 4.1 C
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O, U Stresses Physical/Infrastructu
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Manuscript for IEEE Transactions on
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Page 106 and 107: 2 discuss problem domain and in Sec
Page 108 and 109: 4 t Si t ∫ + ( ) i ) t When RA i
Page 110 and 111: 6 S UB i ∑ = P LI / LI . (16) i p
Page 112 and 113: 8 policies while underutilizing in
Page 114 and 115: architecture based on both the Grid
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Page 118 and 119: component is a member of one of the
Page 120 and 121: denotes the immediate predecessors
Page 122 and 123: limit of large number of tasks. If
Page 124 and 125: Min-min heuristic algorithm Step 1:
Page 126 and 127: We set up eight different experimen
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Page 156 and 157: Coordinating Control Decisions of S
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Page 160 and 161: Understanding Agent Societies Using
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Page 166 and 167: within the monitored enclave. With
Page 168 and 169: Figure 1. Agent Hierarchy in CPE So
Page 170 and 171: Figure 3. The World Model CPY Agent
Page 172 and 173: Table 2. TechSpecs: Infrastructure
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Page 176 and 177: Acknowledgements The work described
Page 178 and 179: key realization to tackle this prob
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the system starts to perform self-s
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sustained in a supply chain. Resour
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which are necessary to make good mo
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focusing on the computational compl
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line of research that deals with ne
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ealizable. But the inherent complex
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Min, H. and Zhou, G., 2002, Supply
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In the rest of this paper we report
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shows relatively irregular behavior
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